By 2025, 463 according to some estimates, data outputs will be created every day. (For perspective, an exabyte of storage could contain 50,000 years of DVD-quality videos.) It’s now easier than ever to translate physical and digital actions into data, and companies of all kinds have rushed to gather as much data as possible for to gain a competitive advantage.
However, in our collective passion for data (and obtaining more data), what is often overlooked is the role that storytelling plays in extracting real value from data.
The reality is that the data themselves are insufficient to truly influence human behavior. Whether the goal is to improve a company’s results or to persuade people to stay home in the middle of a pandemic, the narrative constrains the action, rather than the numbers alone. As more data is collected and analyzed, communication and storytelling will become even more integrated into the discipline of data science due to their role in separating the noise signal.
Data alone does not stimulate innovation – rather, it is data-driven stories that help uncover hidden trends, personalize power, and streamline processes.
However, this may be an area where data scientists are struggling. In the 2020 Anaconda survey of more than 2,300 data scientists, nearly a quarter of respondents said their data science or machine learning (ML) teams lacked communication skills. This may be one of the reasons why about 40% of respondents said they were able to effectively demonstrate the impact of the business “only sometimes” or “almost never”.
Best data practitioners need to be as good at storytelling as they are at coding and implementing models – and yes, this extends beyond creating visualizations to accompany reports. Here are some recommendations for how data scientists can situate their results in larger contextual narratives.
Make the abstract more tangible
Growing data sets help machine learning models to better understand the scope of problem space, but more data does not necessarily help human understanding. Even for the leftist brain of thinkers, it is not in our nature to understand large abstract numbers or things like marginal improvements in accuracy. This is why it is important to include reference points in your story that make the data tangible.
For example, throughout the pandemic, we have been bombarded with countless statistics on the number of cases, death rates, positivity rates and more. While all of this data is important, tools such as interactive maps and conversations around reproduction numbers are more effective than massive data repositories in providing context, risk transmission, and consequently contributing to changing behaviors as they come. necessary. By working with figures, data practitioners have a responsibility to provide the necessary structure so that the data can be understood by the target audience.